01 Key Concepts
Eigenvector Definition
A nonzero vector v satisfying A*v = lambda*v for some eigenvalue lambda; the matrix only scales v, never changes its direction.
Finding Eigenvectors
For each eigenvalue lambda, solve the system (A - lambda*I)*v = 0 to find the corresponding eigenvector(s).
Eigenvectors Are Not Unique
Any nonzero scalar multiple of an eigenvector is also a valid eigenvector for the same eigenvalue — eigenvectors define a direction, not a single fixed vector.
Eigenspace
The set of all eigenvectors for a given eigenvalue (plus the zero vector) forms a subspace called the eigenspace for that eigenvalue.
Diagonalization (Concept)
If a matrix has enough independent eigenvectors, it can be rewritten in a simpler diagonal form using those eigenvectors, making many calculations far easier.
02 Key Formulas
- (A - lambda*I) * v = 0
03 Solved Examples
- Compute A - 5I = [[4-5,1],[2,3-5]] = [[-1,1],[2,-2]].
- Solve (A-5I)*v=0: -x+y=0, so y=x.
- Choose x=1, giving y=1.
- Compute A - 2I = [[4-2,1],[2,3-2]] = [[2,1],[2,1]].
- Solve (A-2I)*v=0: 2x+y=0, so y=-2x.
- Choose x=1, giving y=-2.
- Check if (2,2) is a scalar multiple of (1,1).
- (2,2) = 2*(1,1), so yes, it's a scalar multiple.
04 Practice Questions
📄 Eigenvectors — Downloadable Worksheet
10 questions with a full answer key. Grab the PDF to print, or try the interactive version in your browser.